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Mohamed",authors:[{id:"42713",title:"Dr.",name:"Sunday",middleName:null,surname:"Ekesi",fullName:"Sunday Ekesi",slug:"sunday-ekesi"},{id:"53233",title:"Dr.",name:"Samira A.",middleName:null,surname:"Mohamed",fullName:"Samira A. Mohamed",slug:"samira-a.-mohamed"}]},{id:"23747",title:"Quality Control of Baculoviral Bioinsecticide Production",slug:"quality-control-of-baculoviral-bioinsecticide-production",signatures:"Solange Ana Belén Miele, Mariano Nicolás Belaich and Pablo Daniel Ghiringhelli",authors:[{id:"52118",title:"Dr.",name:"Pablo Daniel",middleName:null,surname:"Ghiringhelli",fullName:"Pablo Daniel Ghiringhelli",slug:"pablo-daniel-ghiringhelli"},{id:"54727",title:"Mrs.",name:"Solange Ana Belen",middleName:null,surname:"Miele",fullName:"Solange Ana Belen Miele",slug:"solange-ana-belen-miele"},{id:"54728",title:"Dr.",name:"Mariano Nicolas",middleName:null,surname:"Belaich",fullName:"Mariano Nicolas Belaich",slug:"mariano-nicolas-belaich"}]},{id:"23748",title:"Quality Control and Characterization of Scintillating Crystals for High Energy Physics and Medical Applications",slug:"quality-control-and-characterization-of-scintillating-crystals-for-high-energy-physics-and-medical-a",signatures:"Daniele Rinaldi, Michel Lebeau, Nicola Paone, Lorenzo Scalise and Paolo Pietroni",authors:[{id:"49866",title:"Ph.D.",name:"Lorenzo",middleName:null,surname:"Scalise",fullName:"Lorenzo Scalise",slug:"lorenzo-scalise"},{id:"54920",title:"Prof.",name:"Daniele",middleName:null,surname:"Rinaldi",fullName:"Daniele Rinaldi",slug:"daniele-rinaldi"},{id:"59026",title:"Dr.",name:"Michel",middleName:null,surname:"Lebeau",fullName:"Michel Lebeau",slug:"michel-lebeau"},{id:"59027",title:"Prof.",name:"Nicola",middleName:null,surname:"Paone",fullName:"Nicola Paone",slug:"nicola-paone"},{id:"78731",title:"Dr.",name:"Paolo",middleName:null,surname:"Pietroni",fullName:"Paolo Pietroni",slug:"paolo-pietroni"}]},{id:"23749",title:"Effect of Last Generation Additives on the Concrete Durability",slug:"effect-of-last-generation-additives-on-the-concrete-durability",signatures:"Ana M. Carvajal, M. Soledad Gómez, Pablo Maturana and Raul Molina",authors:[{id:"42072",title:"Prof.",name:"Ana",middleName:"MarÃa",surname:"Carvajal",fullName:"Ana Carvajal",slug:"ana-carvajal"},{id:"42100",title:"Prof.",name:"Pablo",middleName:null,surname:"Maturana",fullName:"Pablo Maturana",slug:"pablo-maturana"},{id:"84065",title:"Prof.",name:"Soledad",middleName:null,surname:"Gomez",fullName:"Soledad Gomez",slug:"soledad-gomez"},{id:"84067",title:"Mr.",name:"Raul",middleName:null,surname:"Molina",fullName:"Raul Molina",slug:"raul-molina"}]},{id:"23750",title:"A Convenient and Inexpensive Quality Control Method for Examining the Accuracy of Conjugate Cam Profiles",slug:"a-convenient-and-inexpensive-quality-control-method-for-examining-the-accuracy-of-conjugate-cam-prof",signatures:"Wen-Tung Chang and Long-Iong Wu",authors:[{id:"43114",title:"Prof.",name:"Wen-Tung",middleName:null,surname:"Chang",fullName:"Wen-Tung Chang",slug:"wen-tung-chang"},{id:"51808",title:"Prof.",name:"Long-Iong",middleName:null,surname:"Wu",fullName:"Long-Iong Wu",slug:"long-iong-wu"}]},{id:"23751",title:"Material Characterization and Failure Analysis for Microelectronics Assembly Processes",slug:"material-characterization-and-failure-analysis-for-microelectronics-assembly-processes",signatures:"Chien-Yi Huang, Ming-Shu Li, Shan-Yu Huang, Cheng-I Chang and Min-Hui Huang",authors:[{id:"52412",title:"Dr.",name:"Ming-Shu",middleName:null,surname:"Li",fullName:"Ming-Shu Li",slug:"ming-shu-li"},{id:"52426",title:"Prof.",name:"Chien-Yi",middleName:"Jay",surname:"Huang",fullName:"Chien-Yi Huang",slug:"chien-yi-huang"},{id:"98841",title:"MSc.",name:"Cheng-I",middleName:null,surname:"Chang",fullName:"Cheng-I Chang",slug:"cheng-i-chang"},{id:"98978",title:"BSc.",name:"Min Hui",middleName:null,surname:"Huang",fullName:"Min Hui Huang",slug:"min-hui-huang"},{id:"99353",title:"MSc.",name:"Shan-Yu",middleName:null,surname:"Huang",fullName:"Shan-Yu Huang",slug:"shan-yu-huang"}]}]}],publishedBooks:[{type:"book",id:"95",title:"Applications and Experiences of Quality Control",subtitle:null,isOpenForSubmission:!1,hash:"4bcb22b1eee68210a977a97d5a0f363a",slug:"applications-and-experiences-of-quality-control",bookSignature:"Ognyan Ivanov",coverURL:"https://cdn.intechopen.com/books/images_new/95.jpg",editedByType:"Edited by",editors:[{id:"22230",title:"Prof.",name:"Ognyan",surname:"Ivanov",slug:"ognyan-ivanov",fullName:"Ognyan Ivanov"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"632",title:"Wide Spectra of Quality Control",subtitle:null,isOpenForSubmission:!1,hash:"9f7ce64f86daee44a8c5604e8924de1c",slug:"wide-spectra-of-quality-control",bookSignature:"Isin Akyar",coverURL:"https://cdn.intechopen.com/books/images_new/632.jpg",editedByType:"Edited by",editors:[{id:"36323",title:"Dr.",name:"Isin",surname:"Akyar",slug:"isin-akyar",fullName:"Isin Akyar"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"3074",title:"Practical Concepts of Quality Control",subtitle:null,isOpenForSubmission:!1,hash:"3bb079fbda0a27f91cd90dcbd0cc3a6b",slug:"practical-concepts-of-quality-control",bookSignature:"Mohammad Saber Fallah Nezhad",coverURL:"https://cdn.intechopen.com/books/images_new/3074.jpg",editedByType:"Edited by",editors:[{id:"150393",title:"Dr.",name:"Mohammad Saber Fallah",surname:"Nezhad",slug:"mohammad-saber-fallah-nezhad",fullName:"Mohammad Saber Fallah Nezhad"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited 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Navas",coverURL:"https://cdn.intechopen.com/books/images_new/9925.jpg",editedByType:"Edited by",editors:[{id:"19636",title:"Prof.",name:"Pengzhong",surname:"Li",slug:"pengzhong-li",fullName:"Pengzhong Li"}],equalEditorOne:{id:"178637",title:"Dr.",name:"Paulo",surname:"Pereira",slug:"paulo-pereira",fullName:"Paulo Pereira"},equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}}],publishedBooksByAuthor:[{type:"book",id:"95",title:"Applications and Experiences of Quality Control",subtitle:null,isOpenForSubmission:!1,hash:"4bcb22b1eee68210a977a97d5a0f363a",slug:"applications-and-experiences-of-quality-control",bookSignature:"Ognyan Ivanov",coverURL:"https://cdn.intechopen.com/books/images_new/95.jpg",editedByType:"Edited by",editors:[{id:"22230",title:"Prof.",name:"Ognyan",surname:"Ivanov",slug:"ognyan-ivanov",fullName:"Ognyan Ivanov"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}}]},onlineFirst:{chapter:{type:"chapter",id:"81209",title:"Phylogeny and Population Genetic Structure of Minke Whales Worldwide: A Review of Recent Studies",doi:"10.5772/intechopen.102675",slug:"phylogeny-and-population-genetic-structure-of-minke-whales-worldwide-a-review-of-recent-studies",body:'Minke whales are members of the Order Cetacea. They are the smallest species within the suborder Mysticeti (baleen whales), usually not exceeding the 10 m in body length. They are characterized by a sharply pointed head that looks V-shaped when see from above, and they present a sharp longitudinal ridge that runs along the top of the rostrum [1]. Minke whales are the most abundant of the baleen whales and they are hunted in limited numbers by some countries for commercial (Japan and Norway) or aboriginal subsistence (Greenland) purposes.
Until relatively recently, only one species of minke whale was thought to exist:
In 1998, based on a review of both morphological and genetic data, two species of minke whales were recognized, the Antarctic minke whale (
External morphology of minke whales. From top to bottom: Antarctic minke whale, North Pacific common minke whale, North Atlantic common minke whale and Southern Hemisphere common minke whale (dwarf minke whale).
Several genetic studies of minke whales have been conducted since the 1998 review. Some studies have focused on phylogenetic issues while others have focused on elucidating population genetic structure in each oceanic basin. This chapter aims to provide a short review of recent genetic studies, outlining the main new findings and implications. After introducing the genetic markers in Section 2, in Section 3, we review the studies that focus primarily on phylogeny and other matters that are relevant to taxonomy and then, in Section 4, we concentrate on the studies on the population genetic structure of each species and sub-species by oceanic basin (Southern Hemisphere, North Atlantic and North Pacific).
Both information on taxonomy and population identification and structure of minke whales are important and necessary for effective decision-making about conservation and sustainable use of the species.
Two main genetic markers have been used in recent genetic analyses of minke whales, mitochondrial DNA (mtDNA) control region sequences and microsatellite DNA (msDNA, a nuclear marker) genotypes, which are briefly explained here based on [11].
The mitochondrial genome is a circular, double-stranded molecule ranging in size from 16,500 to 17,600 base-pairs (bp) in cetaceans. The main features of mtDNA are (a) maternal inheritance, (b) no recombination during reproduction and (c) it is haploid. Features (a) and (c) mean that the effective population size for the mtDNA genome is ¼ of that for nuclear markers. Sequence changes in animal mitochondrial genomes are of four types: sequence arrangements; additions; deletions; and nucleotide substitutions. The substitution rate is not constant across the mitochondrial genome. The most variable part is where replication begins (the ‘control region’). The control region is the only major non-coding region in the mitochondrial genome. In whales, its length is approximately 1000 bp. In most studies on minke whales, the sequence of the first 300-500 bp in the control region is determined, which is the most variable part.
MsDNA or simple tandem repeats (STRs) are segments of non-coding nuclear DNA containing a varying number (different alleles) of tandem repeats of short sequences of less than six nucleotides. As a nuclear marker, they are diploid with recombination during reproduction. They are abundant and widely distributed throughout the mammalian genome. MsDNA is highly variable, presenting a large number of alleles at each locus, selectively neutral, inherited in standard Mendelian fashion and allelically codominant. MsDNA generally evolves by changes in the number of repeats, i.e., in the length of the repetitive region. MsDNA alleles can be distinguished by differences in the length of the repetitive region. They predominantly mutate by insertion or deletion of repeats. In most studies on minke whales, a set of approximately 12–16 msDNA loci are used.
Most of the recent genetic studies on minke whales have made combined use of these two genetic markers, which presents several advantages. Some of the genetic criteria for taxonomic definition require results of both markers (see below). Different species of large whales can produce hybrid whales and such cases can be detected by the combined use of mtDNA and msDNA. In studies on population identification and structure, parallel analyses of Mendelian and maternally inherited loci are particularly important. Some species may display maternally directed phylopatry. In such cases, genetic differences can be found for the mtDNA but not for msDNA. The use of msDNA in addition to mtDNA allows for an investigation of kinship, which is important information for the interpretation of population structure.
Details of laboratory procedures for mtDNA and msDNA in minke whales can be found in [12].
Several genetic studies addressing phylogenetic and other aspects relevant for taxonomy were conducted after the 1998 review in [10]. All those studies used samples from minke whale worldwide [13, 14, 15, 16, 17, 18]. Oceanic basins covered by the genetic sampling in recent studies are shown in Figure 2.
Oceanic basins covered by the genetic sampling for the phylogenetic and other studies relevant for the taxonomy of minke whales. SOJ = Sea of Japan, NA = North Atlantic, WSP=Western South Pacific, WSA = Western South Atlantic, WNP=Western North Pacific, Antarctic minke whale = Antarctic Ocean (modified from [
A brief description and main findings of these studies are presented below. Several phylogenetic inference methods were used to evaluate observed heritable traits, such as mtDNA sequences, under a specified model of the evolution of the traits. Taxonomic classification is now usually based on phylogenetic data. Details of the phylogenetic inference methods are not given here however relevant bibliographic references on the methods are provided for interested readers in the sections below.
The focus of the first post-1998 study involving minke whales was a case study to investigate the radiation and speciation of pelagic organisms during the period of global warming [13]. The study was based on mtDNA control region sequences (340 bp) in samples of Antarctic minke whales (n = 180), North Atlantic (n = 102) and North Pacific (n = 161) common minke whales and Southern Hemisphere common or dwarf minke whales (n = 23 from the western South Atlantic, WSA and western South Pacific, WSP). A total of 187 haplotypes (unique sequences) were determined. The genealogical relationship among a sub-set of 60 haplotypes was estimated using the NUCML program in the MOLPHY computer package [19], the BASEML program in the PAML computer package [20] and the TREE-PUZZLE program of the quartet-puzzling (QP) method [21]. Divergence time was estimated by applying a molecular clock model using a calibration point that minke whales and the gray whales (
The study provided evidence for phylogenetic differentiation not only between the two species of minke whales but also among North Atlantic, North Pacific and Southern Hemisphere common minke whales. The study estimated that the two species of minke whales diverged in the Southern Hemisphere less than 5 Ma, and that the current sub-species of the common minke whales diverged after the Pliocene some 1.5 Ma. Based on their analysis, the authors hypothesized that prolonged periods of global warming facilitate speciation in pelagic marine species that depend on upwelling [13].
Three relevant studies are described here [14, 16, 18]. The first study [14] used mtDNA control region sequences (327 bp) and a similar sample set of the previous study [13] but this time the study was focused to elucidate the population genetic structure of the Southern Hemisphere common minke whales using samples from WSA (n = 12) and WSP (n = 17) (Figure 3).
The geographic position of Southern Hemisphere common minke whales (dwarf minke whales) samples used in [
The genealogy of the mtDNA haplotypes was estimated using the neighbor-joining method (NJ) [23], minimum evolution (ME) [24], maximum likelihood (ML) [25] and maximum parsimony (MP) [26]. To evaluate the relative effects of divergence and migration between WSA and WSP whales, the approach in [27] modified for a finite mutation level [28] was used. Phylogenetic inferences derived from these methods were consistent, and similar to the inferences obtained in a previous study [13]. WSA common minke whale haplotypes (except one), clustered in a single clade, which nested within the North Atlantic common minke whale clade. On the other hand, WSP common minke whale haplotypes clustered in a different clade. The study showed that haplotypes from the WSA whales share more recent common ancestors with the North Atlantic minke whales than they do with the WSP minke whales. The analysis suggested a very low number of migrants by generation between WSA and WSP, which suggests that the WSA single haplotype in the WSP clade was unlikely to be a result of migration but rather due to incomplete lineage sorting [14].
The most recent genetic analysis on minke whales worldwide [18] was based on mtDNA control region sequences (313 bp) and msDNA (11 loci). The sample set for the mtDNA analysis was similar to those in the previous studies [13, 14] but the samples of the Southern Hemisphere common minke whales were increased (WSP, n = 17; WSA, n = 30), and msDNA was used in addition to mtDNA. A total of 148 haplotypes were determined. The genealogy of the mtDNA haplotypes was estimated using several methods including NJ, ML and Bayesian inferences (BI) [29]. The three methods provided similar results, and they were consistent with previous phylogenetic inferences [13, 14]. Results from the BI method are shown in Figure 4. This figure shows two main clades, one corresponding to Antarctic minke whales and the other to common minke whales. Furthermore, within the common minke whales clade, North Pacific, North Atlantic and Southern Hemisphere common minke whales clustered in different sub-clades.
Bayesian phylogenetic tree of minke whale mtDNA haplotypes. Values indicate support for each node according to the maximum posterior probabilities>80%. Scale bar represents substitutions per nucleotide site. NA = North Atlantic; WSA: Western South Atlantic; WSP = Western South Pacific; SOJ = Sea of Japan; WNP = Western North Pacific (modified from [
Figure 4 shows that WSA and WSP common minke whales in the Southern Hemisphere clustered in different sub-clades (except the single WSA haplotype mentioned previously that clustered within the WSP sub-clade), and that the WSA haplotypes fell with the North Atlantic sub-clade.
This study estimated the net nucleotide substitutions (
The msDNA analysis in [18] involved samples from three localities only (unfortunately, no samples from the North Atlantic common minke whales were considered): North Pacific and Southern Hemisphere (WSA and WSP) common minke whales. The pattern of msDNA differentiation was investigated by two indices,
Although, the third study was focused to investigate hybrids between the two species of minke whales [16], it also provided information on genetic differentiation between the Antarctic and common minke whales species as well among common minke whales from different oceanic basins. The study was based on mtDNA control region sequences (287 bp) and msDNA (11 loci), and samples from the Antarctic minke whale (n = 91), North Atlantic (n = 91), North Pacific (n = 95) and Southern Hemisphere (WSP) (n = 9) common minke whales. The genealogy of the mtDNA haplotype was estimated using the NJ method and the inferences obtained were similar to the other studies [13, 14, 18]. The msDNA
A genetic study based on both mtDNA (287 bp) control region sequences and msDNA (13 loci) reported the migration of an Antarctic minke whale into the Arctic Northeast Atlantic in 1996 [15]. The same study reported the occurrence of a hybrid whale in the North Atlantic in 2007. The analytical procedures for the identification of the hybrid involved the use of the Bayesian cluster analysis
Taxonomic definitions are associated with the term Evolutionary Significant Unit (ESU) [36, 37], defined in [37] as ‘ESUs should be reciprocally monophyletic for mitochondrial DNA alleles and show significant divergence of allele frequencies at nuclear loci’. However, other authors have argued that the definition of ESUs should incorporate ecological data in addition to data on genetic variation of adaptive significance [38]. An example of ecological data could be discrete prey preferences of sympatric individuals. Other authors suggest the use of
Considering these criteria, the post-1998 genetic results (with larger sample sizes and wider geographical range), strongly support the division of Antarctic and common minke whales as different species [10]. They clearly match the ESU definition (based on different phylogenetic inference methods), and the average estimated
Within the common minke whales, the North Pacific and Southern Hemisphere (WSP) match the ESU criterion. Their average
The case of the North Atlantic and Southern Hemisphere (WSA) common minke whales is more complex. This is because some of the mtDNA phylogenetic analyses showed haplotypes of common minke whales from WSA clustering within the North Atlantic common minke whale clade, therefore not matching the reciprocally monophyletic for mitochondrial DNA definition of ESU, although the status of sub-species is appropriate based on the
Finally, and following the criteria above, whales from the Sea of Japan and western North Pacific should be considered as populations of the North Pacific common minke whale.
The cases of hybridization between minke whale species and the study showing that such hybrids may be fertile, and that they can back-cross have some relevance to the taxonomy of minke whales. As noted in [16], it is not possible to resolve whether the observed migration of Antarctic minke whales to the Arctic, and hybridization between Antarctic minke whales and North Atlantic common minke whales are (a) random events that have occurred over a long period of time; (b) the result of a low number of Antarctic minke whales migrating from the Antarctic to the Arctic in the 1990s; or (c) represent a trend that is increasing in frequency. The authors in [16] further argued that the lack of hybrids in the large (n > 15000) Japanese genetic data sets infers that such events are not frequent. Unless the frequency of reproductive contact increases significantly in the future, the separation of the Antarctic minke whale and the North Atlantic common minke whale should not be challenged [16].
In summary, the recent genetic studies provide support for the classification recognized in the 1998 review [10] for two species, the Antarctic and the common minke whale, and at least three sub-species of the latter. Furthermore, these studies suggest a phylogenetic separation between Southern Hemisphere common minke whales from Western South Pacific and Western South Atlantic. Whales from these two localities differed significantly in mtDNA haplotype and msDNA allele frequencies. Phylogenetic analyses showed that haplotypes from the WSA whales share a more recent common ancestor with the North Atlantic common minke whales than they do with the WSP common minke whales.
Minke whales were hunted commercially or under special permit in the Southern Hemisphere until the 2018/19 austral summer season, and they are hunted currently for limited numbers in the North Atlantic (commercial and aboriginal subsistence purposes), and western North Pacific (commercial purposes). Identification of populations within species and sub-species in each oceanic basin, therefore, is very important for conservation and management purposes. This is because different populations of the same species or sub-species may respond in different ways to levels of direct removals (e.g., catches, bycatches) and other types of environmental stress (e.g., habitat degradation) [18]. Population dynamics modeling is used to investigate the effect of different management strategies and environmental stressors at the population level. However, the identification of populations is not a trivial issue.
In each of the relevant oceanic basins, Southern Hemisphere, North Atlantic and North Pacific, minke whales are believed, like most baleen whale species, to undertake seasonal migrations between feeding grounds in higher latitudes in summer and breeding grounds in lower latitudes in the tropical or temperate regions in winter. However, there are few direct observations of this linkage, and information of minke whale breeding grounds in low latitudes is poor. Ideally, genetic analyses on population identification should be carried out based on samples collected in breeding grounds. However, all genetic analyses on minke whale population identification have been based on samples collected in feeding grounds and migratory corridors, where different populations may mix spatially and/or temporally.
The International Whaling Commission (IWC) has defined areas for the management (i.e., the setting of catch limits) of minke whales in each oceanic basin based upon a variety of data types, genetic and non-genetic (e.g., see [40]) since the earliest days of management, often based upon limited information or analogy. Most recent studies have focused on the correspondence of the set management boundaries with the available genetic information and revising the boundaries as appropriate to ensure that over-exploitation does not occur. The primary management tool used by the IWC Scientific Committee to provide advice on commercial whaling catch limits is known as the Revised Management Procedure or RMP that focusses on providing robust management advice in the light of inevitable scientific uncertainty (e.g., [41]). Uncertainty in stock structure is one of the most influential in terms of providing robust advice. The philosophy adopted under the RMP (and the sister approach for aboriginal subsistence whaling known as the AWMP or Aboriginal Subsistence Whaling Management Procedure) with respect to stock structure is that it is not often, if ever, possible to arrive at only one plausible stock structure hypothesis from the available data. Rather than in the past when the ‘best’ hypothesis (and boundaries) was determined and then fixed management boundaries for the ‘unit-to-conserve’ (usually a population) chosen, the RMP approach says that catch limits must be set that are robust to all plausible hypotheses and that these hypotheses should be regularly reviewed in the light of new data. Of course, deciding what comprises ‘plausible’ is a complex and difficult issue and one which has driven much of the work described below, especially for the North Pacific common minke whale.
In this section, the most recent genetic analyses on population identification and structure in minke whales are reviewed for each species and sub-species in each relevant oceanic basin.
The method most often used for the identification of populations within an oceanic basin was hypothesis testing under the null hypothesis of panmixia. Under this method, mtDNA haplotype and/or msDNA allele frequencies between two geographically grouped samples are compared using several statistical tests. More recently, spatially explicit clustering approaches, for example, sPCA, GENELAND, TESS and BAPS have been used to investigate population identification and structure.
Details of the statistical tests and clustering approaches are not given here however relevant references on the methods are provided for interested readers in the sections below.
The IWC’s management areas for baleen whales (excluding the Bryde’s whale
Management areas defined by the International Whaling Commission for the management of baleen whales (except the Bryde’s whale) in the Southern Hemisphere. These areas were used for the management of the Antarctic minke whale in the period of commercial whaling, which was stopped in the 1986/87 austral summer season. Most of the recent genetic studies on population structure have been conducted in the shaded area.
There are no genetic samples from Antarctic minke whales from low latitude regions of the eastern Indian Ocean and western South Pacific where breeding grounds of this species in this region are assumed to occur. The most recent genetic studies were based therefore on samples collected by the JARPA and JARPAII programs in the Antarctic feeding grounds of Areas III east, IV, V and VI west. Those studies were summarized in [42], and the most relevant aspects are highlighted here.
Previous morphometric, biological and genetic studies based on mtDNA and msDNA led to the conclusion that Antarctic minke whales in the feeding grounds between Areas III east and VI west do not comprise a single population [43]. The most recent genetic study used mtDNA control region sequences (340 bp) and msDNA (12 loci) [12] to examine a total of 2254 samples in the Indo-Pacific sector of the Antarctic: Area III east = 564; Area IV west = 734, Area IV east = 74, Area VE east = 478, Area VI west = 404. The samples were obtained in the Southern Hemisphere summer season in different years. The degree of spatial and temporal divergence was estimated via the
The main conclusion of the studies was the existence of at least two populations in the feeding grounds of the Indo-Pacific sector of the Antarctic and a transition area in the region around 100°-165°E, across which there is an as yet undetermined level and range of mixing (Figure 6). The following names were proposed for these populations: Eastern Indian Ocean Population (I-Population) and Western South Pacific Ocean Population (P-Population) [42].
The current hypothesis of population structure of the Antarctic minke whale. At least two populations occur in the Indo-Pacific sector of the Antarctic covered by the surveys of the JARPA/JARPAII, which mix in a transition area, whose position and extension varies by year and sex. These populations are possibly related to breeding grounds in lower latitudes evidenced by high-density areas suggested by sighting surveys (upper part of figures) (after [
A recent study described a paternity method based on msDNA (12 loci) to estimate the abundance of mature male Antarctic minke whales in the Indo Pacific sector of the Antarctic using a maximum likelihood approach [46]. Results for the geographical distribution of mother/fetus-father pairs were generally consistent with the hypothesis of separate I- and P- Populations because eight of 10 pairs were found in the expected areas of distribution of either population. Only two pairs were found in distant areas.
The genetic studies showed no concordance between the geographic boundaries of the IWC management Areas and the geographical distribution of the I- and P- populations suggested by the genetic analyses.
The IWC’s management areas for North Atlantic common minke whales are shown in Figure 7. In this section, the most recent genetic studies on population structure are summarized [47, 48, 49]. These studies were focused on examining the biological validity of the management areas in Figure 7.
Management subareas used by the International Whaling Commission for the management of commercial and aboriginal subsistence whaling of North Atlantic common minke whales. Sub-areas prefixed by W represent the western North Atlantic, sub-areas prefixed by C represent the central NorthAtlantic and sub-areas prefixed by E represent the eastern North Atlantic. Management subarea EC mentioned in the main text merged into a single EW subarea.
The first study reviewed here [47] was based on genetic samples (n = 306) collected throughout the North Atlantic (see Table 1). Samples were collected in spring-summer over several years. The genetic markers used were mtDNA control region (500 bp) and msDNA (16 loci). The analytical procedures used for mtDNA were the
Management Area | Sample size study [47] | Sample size study [48] | Sample size study [49] |
---|---|---|---|
WG | 166 | 36 | |
WC | 15 | ||
CIC | |||
CG + CM | 54 | 17 (CM only) | 48 (CM only) |
ES, EB, EC | 63 | 48 (ES only) | 1583 (ES + EB only) |
EW | 1013 | ||
EN | 23 | 83 | |
Spain | 3 | ||
Summary of sample sizes by North Atlantic management subareas in the three studies referred to in the text.
The second study [48] was based on smaller sample size (n = 202) but again throughout the North Atlantic (see Table 1). Samples were collected mainly in spring-summer over several years. The genetic markers used were mtDNA control region sequences (345 bp) and msDNA (10 loci). The relevant analytical procedures to investigate population structure based on msDNA were the
The third study [49] was based on much larger sample size (n = 2664) but primarily from the Eastern North Atlantic (Table 1). The genetic markers used were mtDNA control region sequences (331 bp) and msDNA (10 loci). The study used several analytical procedures to investigate population structure based on msDNA including
The IWC’s management sub-areas for North Pacific common minke whales are shown in Figure 8. At least two populations of the common minke whales have been historically recognized in the western North Pacific, (1) the Okhotsk Sea-West Pacific (known in IWC literature as the O-stock) and (2) the Sea of Japan-Yellow Sea-East China Sea (known as the J-stock). There are morphological and reproductive [57, 58] as well genetic [59, 60] characters differentiating these two populations.
Management subareas defined by the International Whaling Commission for the management of the North Pacific common minke whales.
Recent genetic work has focused on refining this two-population hypothesis as well as investigating whether additional structure exists within the J- and O-stocks. Studies have been based on samples collected mainly during the Japanese Whale Research Programs under Special Permit in the western North Pacific, Phases I and II (JARPN and JARPNII) and bycatches along the Japanese coast. Surveys of these research programs were conducted systematically in the western North Pacific in spring-summer from 1994 to 2016. Table 2 summarizes the number of samples used in recent studies, by subarea.
Management Area | Sample size study [61] | Sample size O-stock study [62] |
---|---|---|
2C | 487 | |
6E | 717 | |
10E | 13 | |
11 | 129 | 48 |
7CN | 1066 | 739 |
7CS | 921 | 439 |
7E | 49 | 45 |
7WR | 100 | 89 |
8 | 252 | 223 |
9 | 541 | 487 |
Summary of sample sizes by North Pacific management subareas used in recent studies referred to in the text.
Individual probability assignment to either J- or O-stocks was made possible by the use of
Spatial occurrence of O- and J-stocks in management sub-areas around Japan (see
Figure 10 shows the temporal distribution of the J- and O-stock individuals on the Pacific side of Japan (2C, 7CN and 7CS) expressed as a three-month moving average. In sub-area 2C, J-stock animals are predominant throughout the year. In sub-areas 7CS and 7CN, the proportion of the J-stock increases in autumn/winter and decreases in spring/summer – the reverse is true for O-stock animals.
Monthly occurrence of O- and J-stocks in areas 2C, 7CS and 7CN. Each bar is expressed as three-month moving average. Sample sizes are on the top of each bar. The sampling years in area 2C was 2001–2014; in areas 7CN and 7CS was 1994–2014. ‘Unknown’ refers to individuals that could not be assigned to either stock by STRUCTURE (after [
The phylogenetic tree of haplotypes showed no population-specific clade although most of the individuals assigned to the J-stock shared the same clade. Most of the individuals assigned to the O-stock shared clades where the J-stock individuals were less frequent [61].
A subsequent study investigated the possibility of additional structure within O-stock based on mtDNA control region sequences (487 bp) and msDNA (16 loci) [62]. The sample size of the O-Stock for the different subareas shown in Figure 8 was 2070 (Table 2). The methods used for investigating structure based on msDNA data were the probability test [63] and the discriminant analysis of principal component (DAPC) approach [64]; for the latter analysis, both J- and O-stock assigned individuals were used. For mtDNA, heterogeneity tests in haplotype frequencies among the samples were conducted using both the chi-square test of independence and conventional
A later study used DAPC and spatial analysis of principal component (sPCA) [65] to investigate population structure [66]. The study was based on msDNA (16 loci) and the sample sizes were similar to the previous study [61]. The DAPC failed to find evidence of additional structure other than the J- and O-stocks. The results indicated a low possibility that multiple stocks exist (other than the J- and O-stocks) with overlapping geographic ranges.
A different approach was used in a study that used msDNA data at 16 loci in 4554 whales to infer Parent-Offspring (P-O) relationships using a Maximum-Likelihood approach [67]. Biological information such as the sex and sexual maturity of the whales was used to interpret the genetic results on P-O pairs. The relationship between False Discovery Rate (FDR) and Power (P) was evaluated by simulation. Of 145 inferred P-O pairs (estimated FDR = 0.1), 141 were further evaluated by typing 10 additional msDNA loci. A total of 75 were confirmed (among them 26 Mother-Fetus pairs) and 66 pairs were ranked ‘False Positives’, yielding an overall observed FDR of 0.468. Among the validated P-O pairs, O-stock pairs were significantly overrepresented and no pairs between J- and O-stock individuals were detected. J-stock animals seem to appear on both sides of Japan closer to the coast, while O-stock individuals occur mostly to the east of Japan, both close to the coast and far offshore. The study provided no evidence for further population structure other than J and O-stocks.
Most recently, a study [68] used three spatially explicit clustering tools including GENELAND [69], TESS [70, 71] and BAPS to explore the msDNA data used previously in [66]. The authors believed that the most informative approach was GENELAND using the mixture model with correlated allele frequency model, which supported
Over the last two decades, several important genetic studies focused on investigating population identification and structure in minke whales have been undertaken in three oceanic basins using two genetic markers, mtDNA and msDNA. The driving force behind these analyses was obtaining information to help with effective conservation and management. Of necessity, all of these studies were based on genetic samples collected in feeding grounds and migratory corridors. In this context, population identification is associated with the concept of Management Units (MUs) described by one author in 1994 as ‘populations with a significant divergence of allele frequencies at nuclear or mitochondrial loci, regardless of the phylogenetic distinctiveness of the alleles’ [37]. Several of the studies described above presented statistical results that are consistent with this criterion for defining the population. In addition to hypothesis testing, several increasingly sophisticated clustering approaches have been used for the purpose of identifying populations.
Recent studies in the Southern Hemisphere were concentrated in the Indo-Pacific sector of the Antarctic where a large number of genetic samples of Antarctic minke whales was available from Japanese whale research programs. At least two populations have been identified in this sector, the I- and P-populations, which may be related to breeding grounds in lower latitudes of the eastern Indian Ocean and western South Pacific. These populations exhibit significant differences in their mtDNA haplotype and msDNA alleles frequencies, matching the criterion for Management Unit defined above. The Australian continent may play a role in isolating these populations during the winter breeding season, with whales presenting some degree of fidelity to particular feeding grounds in the Antarctic during summer. Although, a transition area of mixing of these two populations was postulated in the Antarctic feeding grounds, whales from each population appear to return to their respective breeding grounds in winter.
To fully understand population structure in the Southern Hemisphere, additional effort should be made to collect genetic samples from other sectors of the Antarctic and other regions of the Southern Hemisphere. This will allow investigation of the full distribution of the P- and I-populations as well the research into structure in the remaining sectors of the Antarctic. Clearly, any understanding of population structure will be greatly facilitated by dedicated efforts to investigate the migration routes and locations of breeding areas; satellite tracking will be an extremely valuable tool in this regard [74].
In the North Atlantic, the results of several genetic studies on population identification and structure may appear contradictory. While some studies suggested subtle genetic differences among groups of whales, others studies based on larger sample sizes have failed to detect any evidence of structure in this oceanic basin. As in the Southern Hemisphere, research on migratory routes and locations of breeding grounds is required to assist the interpretation of the results of the genetic analyses in the feeding ground and migratory routes.
In the North Pacific, recent genetic analyses have been concentrated in the western side due to a larger availability of genetic samples from the Japanese whale research programs and to management needs within the context of the IWC’s Scientific Committee. Historically two populations have been recognized in the western North Pacific, the J- and O-stocks, and recent genetic analyses have confirmed their existence and furthermore have revealed more information on their patterns of spatial and seasonal movement. The J-stock occurs mainly in the Sea of Japan although some individuals migrate seasonally to the Pacific side of Japan. The O-stock is mainly found on the Pacific side of Japan. The objective of most recent studies has been to whether or not additional structure occurs within either or both of the J- and O-stocks, and several new analytical approaches were used to respond that question. Results of most of the approaches indicated a lack of additional structure, other than that attributed to the J- and O- stocks. The most recent IWC Scientific Committee discussions allocated high plausibility to the hypothesis of two populations with spatial/temporal mixing in the western North Pacific [75]. As for the other two ocean basins, effort should be made to collect and analyze genetic samples from the less understood eastern North Pacific as well to undertake focused research to understand migratory corridors and breeding ground locations.
It is also important to make effort to investigate the occurrence, distribution and population structure of common minke whales distributed around Chinese and Korean Peninsula waters, and the genetic relationship with whales distributed in the subareas around Japan. Investigation of the population genetic structure in those waters is important as several annual bycatches have been reported for the Korean Peninsula.
Many genetic studies on minke whales were conducted in the last 20 years. New taxonomic information post-1998 relates primarily to the Southern Hemisphere common minke whales (dwarf minke whales) from the western South Pacific and western South Atlantic, which are differentiated by both mtDNA and msDNA markers. The paraphyletic relationship between the North Atlantic and Southern Hemisphere (WSA) common minke whale has important implications for the taxonomic definition of common minke whales. Regarding population genetic structure, at least two populations of the Antarctic minke whale have been identified in the Indo-Pacific sector of the Antarctic, and at least two populations were confirmed in the western North Pacific common minke whales. In the North Atlantic genetic studies suggest that population structure, should it exist, is rather subtle. As for the North Pacific and Southern Hemisphere, analyses are hindered by a lack of knowledge (and thus samples from) breeding grounds.
The population structure of minke whales is intertwined with some degree of fidelity to specific feeding grounds. This fidelity could vary depending on changing short- and long-term environmental conditions. In the case of the Antarctic minke whales, the pattern of distribution and movement of different populations in the feeding grounds has been related with the distribution of their key prey species, the krill (
We thank crew members, researchers and many other persons for the collection of genetic samples from minke whales from different sources, which made possible the studies reviewed in this chapter. We also thank Greg Donovan, former Head of Science of the International Whaling Commission, for useful comments and suggestions that improved substantially the previous version of this chapter. Our appreciation to Jorge Acevedo and Lucas Milmann for their assistance in figure drawing.
All authors declare that there is no conflict of interest.
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He has both an MS and Ph.D. in Biomedical Engineering. He was previously a research scientist at the University of California Los Angeles (UCLA) and visiting professor and researcher at the University of North Dakota. He is currently working in artificial intelligence and its applications in medical signal processing. In addition, he is using digital signal processing in medical imaging and speech processing. Dr. Asadpour has developed brain-computer interfacing algorithms and has published books, book chapters, and several journal and conference papers in this field and other areas of intelligent signal processing. He has also designed medical devices, including a laser Doppler monitoring system.",institutionString:"Kaiser Permanente Southern California",institution:null},{id:"169608",title:"Prof.",name:"Marian",middleName:null,surname:"Găiceanu",slug:"marian-gaiceanu",fullName:"Marian Găiceanu",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/169608/images/system/169608.png",biography:"Prof. Dr. Marian Gaiceanu graduated from the Naval and Electrical Engineering Faculty, Dunarea de Jos University of Galati, Romania, in 1997. He received a Ph.D. (Magna Cum Laude) in Electrical Engineering in 2002. Since 2017, Dr. Gaiceanu has been a Ph.D. supervisor for students in Electrical Engineering. He has been employed at Dunarea de Jos University of Galati since 1996, where he is currently a professor. Dr. Gaiceanu is a member of the National Council for Attesting Titles, Diplomas and Certificates, an expert of the Executive Agency for Higher Education, Research Funding, and a member of the Senate of the Dunarea de Jos University of Galati. He has been the head of the Integrated Energy Conversion Systems and Advanced Control of Complex Processes Research Center, Romania, since 2016. He has conducted several projects in power converter systems for electrical drives, power quality, PEM and SOFC fuel cell power converters for utilities, electric vehicles, and marine applications with the Department of Regulation and Control, SIEI S.pA. (2002–2004) and the Polytechnic University of Turin, Italy (2002–2004, 2006–2007). He is a member of the Institute of Electrical and Electronics Engineers (IEEE) and cofounder-member of the IEEE Power Electronics Romanian Chapter. He is a guest editor at Energies and an academic book editor for IntechOpen. He is also a member of the editorial boards of the Journal of Electrical Engineering, Electronics, Control and Computer Science and Sustainability. Dr. Gaiceanu has been General Chairman of the IEEE International Symposium on Electrical and Electronics Engineering in the last six editions.",institutionString:'"Dunarea de Jos" University of Galati',institution:{name:'"Dunarea de Jos" University of Galati',country:{name:"Romania"}}},{id:"4519",title:"Prof.",name:"Jaydip",middleName:null,surname:"Sen",slug:"jaydip-sen",fullName:"Jaydip Sen",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/4519/images/system/4519.jpeg",biography:"Jaydip Sen is associated with Praxis Business School, Kolkata, India, as a professor in the Department of Data Science. His research areas include security and privacy issues in computing and communication, intrusion detection systems, machine learning, deep learning, and artificial intelligence in the financial domain. He has more than 200 publications in reputed international journals, refereed conference proceedings, and 20 book chapters in books published by internationally renowned publishing houses, such as Springer, CRC press, IGI Global, etc. Currently, he is serving on the editorial board of the prestigious journal Frontiers in Communications and Networks and in the technical program committees of a number of high-ranked international conferences organized by the IEEE, USA, and the ACM, USA. He has been listed among the top 2% of scientists in the world for the last three consecutive years, 2019 to 2021 as per studies conducted by the Stanford University, USA.",institutionString:"Praxis Business School",institution:null},{id:"320071",title:"Dr.",name:"Sidra",middleName:null,surname:"Mehtab",slug:"sidra-mehtab",fullName:"Sidra Mehtab",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y00002v6KHoQAM/Profile_Picture_1584512086360",biography:"Sidra Mehtab has completed her BS with honors in Physics from Calcutta University, India in 2018. She has done MS in Data Science and Analytics from Maulana Abul Kalam Azad University of Technology (MAKAUT), Kolkata, India in 2020. Her research areas include Econometrics, Time Series Analysis, Machine Learning, Deep Learning, Artificial Intelligence, and Computer and Network Security with a particular focus on Cyber Security Analytics. Ms. Mehtab has published seven papers in international conferences and one of her papers has been accepted for publication in a reputable international journal. She has won the best paper awards in two prestigious international conferences – BAICONF 2019, and ICADCML 2021, organized in the Indian Institute of Management, Bangalore, India in December 2019, and SOA University, Bhubaneswar, India in January 2021. Besides, Ms. Mehtab has also published two book chapters in two books. Seven of her book chapters will be published in a volume shortly in 2021 by Cambridge Scholars’ Press, UK. Currently, she is working as the joint editor of two edited volumes on Time Series Analysis and Forecasting to be published in the first half of 2021 by an international house. Currently, she is working as a Data Scientist with an MNC in Delhi, India.",institutionString:"NSHM College of Management and Technology",institution:null},{id:"226240",title:"Dr.",name:"Andri Irfan",middleName:null,surname:"Rifai",slug:"andri-irfan-rifai",fullName:"Andri Irfan Rifai",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/226240/images/7412_n.jpg",biography:"Andri IRFAN is a Senior Lecturer of Civil Engineering and Planning. He completed the PhD at the Universitas Indonesia & Universidade do Minho with Sandwich Program Scholarship from the Directorate General of Higher Education and LPDP scholarship. He has been teaching for more than 19 years and much active to applied his knowledge in the project construction in Indonesia. His research interest ranges from pavement management system to advanced data mining techniques for transportation engineering. He has published more than 50 papers in journals and 2 books.",institutionString:null,institution:{name:"Universitas Internasional Batam",country:{name:"Indonesia"}}},{id:"314576",title:"Dr.",name:"Ibai",middleName:null,surname:"Laña",slug:"ibai-lana",fullName:"Ibai Laña",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/314576/images/system/314576.jpg",biography:"Dr. Ibai Laña works at TECNALIA as a data analyst. He received his Ph.D. in Artificial Intelligence from the University of the Basque Country (UPV/EHU), Spain, in 2018. He is currently a senior researcher at TECNALIA. His research interests fall within the intersection of intelligent transportation systems, machine learning, traffic data analysis, and data science. He has dealt with urban traffic forecasting problems, applying machine learning models and evolutionary algorithms. He has experience in origin-destination matrix estimation or point of interest and trajectory detection. Working with large volumes of data has given him a good command of big data processing tools and NoSQL databases. He has also been a visiting scholar at the Knowledge Engineering and Discovery Research Institute, Auckland University of Technology.",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"314575",title:"Dr.",name:"Jesus",middleName:null,surname:"L. Lobo",slug:"jesus-l.-lobo",fullName:"Jesus L. Lobo",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/314575/images/system/314575.png",biography:"Dr. Jesús López is currently based in Bilbao (Spain) working at TECNALIA as Artificial Intelligence Research Scientist. In most cases, a project idea or a new research line needs to be investigated to see if it is good enough to take into production or to focus on it. That is exactly what he does, diving into Machine Learning algorithms and technologies to help TECNALIA to decide whether something is great in theory or will actually impact on the product or processes of its projects. So, he is expert at framing experiments, developing hypotheses, and proving whether they’re true or not, in order to investigate fundamental problems with a longer time horizon. He is also able to design and develop PoCs and system prototypes in simulation. He has participated in several national and internacional R&D projects.\n\nAs another relevant part of his everyday research work, he usually publishes his findings in reputed scientific refereed journals and international conferences, occasionally acting as reviewer and Programme Commitee member. Concretely, since 2018 he has published 9 JCR (8 Q1) journal papers, 9 conference papers (e.g. ECML PKDD 2021), and he has co-edited a book. He is also active in popular science writing data science stories for reputed blogs (KDNuggets, TowardsDataScience, Naukas). Besides, he has recently embarked on mentoring programmes as mentor, and has also worked as data science trainer.",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"103779",title:"Prof.",name:"Yalcin",middleName:null,surname:"Isler",slug:"yalcin-isler",fullName:"Yalcin Isler",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRyQ8QAK/Profile_Picture_1628834958734",biography:"Yalcin Isler (1971 - Burdur / Turkey) received the B.Sc. degree in the Department of Electrical and Electronics Engineering from Anadolu University, Eskisehir, Turkey, in 1993, the M.Sc. degree from the Department of Electronics and Communication Engineering, Suleyman Demirel University, Isparta, Turkey, in 1996, the Ph.D. degree from the Department of Electrical and Electronics Engineering, Dokuz Eylul University, Izmir, Turkey, in 2009, and the Competence of Associate Professorship from the Turkish Interuniversity Council in 2019.\n\nHe was Lecturer at Burdur Vocational School in Suleyman Demirel University (1993-2000, Burdur / Turkey), Software Engineer (2000-2002, Izmir / Turkey), Research Assistant in Bulent Ecevit University (2002-2003, Zonguldak / Turkey), Research Assistant in Dokuz Eylul University (2003-2010, Izmir / Turkey), Assistant Professor at the Department of Electrical and Electronics Engineering in Bulent Ecevit University (2010-2012, Zonguldak / Turkey), Assistant Professor at the Department of Biomedical Engineering in Izmir Katip Celebi University (2012-2019, Izmir / Turkey). He is an Associate Professor at the Department of Biomedical Engineering at Izmir Katip Celebi University, Izmir / Turkey, since 2019. In addition to academics, he has also founded Islerya Medical and Information Technologies Company, Izmir / Turkey, since 2017.\n\nHis main research interests cover biomedical signal processing, pattern recognition, medical device design, programming, and embedded systems. He has many scientific papers and participated in several projects in these study fields. He was an IEEE Student Member (2009-2011) and IEEE Member (2011-2014) and has been IEEE Senior Member since 2014.",institutionString:null,institution:{name:"Izmir Kâtip Çelebi University",country:{name:"Turkey"}}},{id:"339677",title:"Dr.",name:"Mrinmoy",middleName:null,surname:"Roy",slug:"mrinmoy-roy",fullName:"Mrinmoy Roy",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/339677/images/16768_n.jpg",biography:"An accomplished Sales & Marketing professional with 12 years of cross-functional experience in well-known organisations such as CIPLA, LUPIN, GLENMARK, ASTRAZENECA across different segment of Sales & Marketing, International Business, Institutional Business, Product Management, Strategic Marketing of HIV, Oncology, Derma, Respiratory, Anti-Diabetic, Nutraceutical & Stomatological Product Portfolio and Generic as well as Chronic Critical Care Portfolio. A First Class MBA in International Business & Strategic Marketing, B.Pharm, D.Pharm, Google Certified Digital Marketing Professional. Qualified PhD Candidate in Operations and Management with special focus on Artificial Intelligence and Machine Learning adoption, analysis and use in Healthcare, Hospital & Pharma Domain. Seasoned with diverse therapy area of Pharmaceutical Sales & Marketing ranging from generating revenue through generating prescriptions, launching new products, and making them big brands with continuous strategy execution at the Physician and Patients level. Moved from Sales to Marketing and Business Development for 3.5 years in South East Asian Market operating from Manila, Philippines. Came back to India and handled and developed Brands such as Gluconorm, Lupisulin, Supracal, Absolut Woman, Hemozink, Fabiflu (For COVID 19), and many more. In my previous assignment I used to develop and execute strategies on Sales & Marketing, Commercialization & Business Development for Institution and Corporate Hospital Business portfolio of Oncology Therapy Area for AstraZeneca Pharma India Ltd. Being a Research Scholar and Student of ‘Operations Research & Management: Artificial Intelligence’ I published several pioneer research papers and book chapters on the same in Internationally reputed journals and Books indexed in Scopus, Springer and Ei Compendex, Google Scholar etc. Currently, I am launching PGDM Pharmaceutical Management Program in IIHMR Bangalore and spearheading the course curriculum and structure of the same. I am interested in Collaboration for Healthcare Innovation, Pharma AI Innovation, Future trend in Marketing and Management with incubation on Healthcare, Healthcare IT startups, AI-ML Modelling and Healthcare Algorithm based training module development. I am also an affiliated member of the Institute of Management Consultant of India, looking forward to Healthcare, Healthcare IT and Innovation, Pharma and Hospital Management Consulting works.",institutionString:null,institution:{name:"Lovely Professional University",country:{name:"India"}}},{id:"310576",title:"Prof.",name:"Erick Giovani",middleName:null,surname:"Sperandio Nascimento",slug:"erick-giovani-sperandio-nascimento",fullName:"Erick Giovani Sperandio Nascimento",position:null,profilePictureURL:"https://intech-files.s3.amazonaws.com/0033Y00002pDKxDQAW/ProfilePicture%202022-06-20%2019%3A57%3A24.788",biography:"Prof. Erick Sperandio is the Lead Researcher and professor of Artificial Intelligence (AI) at SENAI CIMATEC, Bahia, Brazil, also working with Computational Modeling (CM) and HPC. He holds a PhD in Environmental Engineering in the area of Atmospheric Computational Modeling, a Master in Informatics in the field of Computational Intelligence and Graduated in Computer Science from UFES. He currently coordinates, leads and participates in R&D projects in the areas of AI, computational modeling and supercomputing applied to different areas such as Oil and Gas, Health, Advanced Manufacturing, Renewable Energies and Atmospheric Sciences, advising undergraduate, master's and doctoral students. He is the Lead Researcher at SENAI CIMATEC's Reference Center on Artificial Intelligence. In addition, he is a Certified Instructor and University Ambassador of the NVIDIA Deep Learning Institute (DLI) in the areas of Deep Learning, Computer Vision, Natural Language Processing and Recommender Systems, and Principal Investigator of the NVIDIA/CIMATEC AI Joint Lab, the first in Latin America within the NVIDIA AI Technology Center (NVAITC) worldwide program. He also works as a researcher at the Supercomputing Center for Industrial Innovation (CS2i) and at the SENAI Institute of Innovation for Automation (ISI Automação), both from SENAI CIMATEC. He is a member and vice-coordinator of the Basic Board of Scientific-Technological Advice and Evaluation, in the area of Innovation, of the Foundation for Research Support of the State of Bahia (FAPESB). He serves as Technology Transfer Coordinator and one of the Principal Investigators at the National Applied Research Center in Artificial Intelligence (CPA-IA) of SENAI CIMATEC, focusing on Industry, being one of the six CPA-IA in Brazil approved by MCTI / FAPESP / CGI.br. He also participates as one of the representatives of Brazil in the BRICS Innovation Collaboration Working Group on HPC, ICT and AI. He is the coordinator of the Work Group of the Axis 5 - Workforce and Training - of the Brazilian Strategy for Artificial Intelligence (EBIA), and member of the MCTI/EMBRAPII AI Innovation Network Training Committee. He is the coordinator, by SENAI CIMATEC, of the Artificial Intelligence Reference Network of the State of Bahia (REDE BAH.IA). He leads the working group of experts representing Brazil in the Global Partnership on Artificial Intelligence (GPAI), on the theme \"AI and the Pandemic Response\".",institutionString:"Manufacturing and Technology Integrated Campus – SENAI CIMATEC",institution:null},{id:"1063",title:"Prof.",name:"Constantin",middleName:null,surname:"Volosencu",slug:"constantin-volosencu",fullName:"Constantin Volosencu",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/1063/images/system/1063.png",biography:"Prof. Dr. Constantin Voloşencu graduated as an engineer from\nPolitehnica University of Timișoara, Romania, where he also\nobtained a doctorate degree. He is currently a full professor in\nthe Department of Automation and Applied Informatics at the\nsame university. Dr. Voloşencu is the author of ten books, seven\nbook chapters, and more than 160 papers published in journals\nand conference proceedings. He has also edited twelve books and\nhas twenty-seven patents to his name. He is a manager of research grants, editor in\nchief and member of international journal editorial boards, a former plenary speaker, a member of scientific committees, and chair at international conferences. His\nresearch is in the fields of control systems, control of electric drives, fuzzy control\nsystems, neural network applications, fault detection and diagnosis, sensor network\napplications, monitoring of distributed parameter systems, and power ultrasound\napplications. He has developed automation equipment for machine tools, spooling\nmachines, high-power ultrasound processes, and more.",institutionString:"Polytechnic University of Timişoara",institution:{name:"Polytechnic University of Timişoara",country:{name:"Romania"}}},{id:"221364",title:"Dr.",name:"Eneko",middleName:null,surname:"Osaba",slug:"eneko-osaba",fullName:"Eneko Osaba",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/221364/images/system/221364.jpg",biography:"Dr. Eneko Osaba works at TECNALIA as a senior researcher. He obtained his Ph.D. in Artificial Intelligence in 2015. He has participated in more than twenty-five local and European research projects, and in the publication of more than 130 papers. He has performed several stays at universities in the United Kingdom, Italy, and Malta. Dr. Osaba has served as a program committee member in more than forty international conferences and participated in organizing activities in more than ten international conferences. He is a member of the editorial board of the International Journal of Artificial Intelligence, Data in Brief, and Journal of Advanced Transportation. He is also a guest editor for the Journal of Computational Science, Neurocomputing, Swarm, and Evolutionary Computation and IEEE ITS Magazine.",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"275829",title:"Dr.",name:"Esther",middleName:null,surname:"Villar-Rodriguez",slug:"esther-villar-rodriguez",fullName:"Esther Villar-Rodriguez",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/275829/images/system/275829.jpg",biography:"Dr. Esther Villar obtained a Ph.D. in Information and Communication Technologies from the University of Alcalá, Spain, in 2015. She obtained a degree in Computer Science from the University of Deusto, Spain, in 2010, and an MSc in Computer Languages and Systems from the National University of Distance Education, Spain, in 2012. Her areas of interest and knowledge include natural language processing (NLP), detection of impersonation in social networks, semantic web, and machine learning. Dr. Esther Villar made several contributions at conferences and publishing in various journals in those fields. Currently, she is working within the OPTIMA (Optimization Modeling & Analytics) business of TECNALIA’s ICT Division as a data scientist in projects related to the prediction and optimization of management and industrial processes (resource planning, energy efficiency, etc).",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"49813",title:"Dr.",name:"Javier",middleName:null,surname:"Del Ser",slug:"javier-del-ser",fullName:"Javier Del Ser",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/49813/images/system/49813.png",biography:"Prof. Dr. Javier Del Ser received his first PhD in Telecommunication Engineering (Cum Laude) from the University of Navarra, Spain, in 2006, and a second PhD in Computational Intelligence (Summa Cum Laude) from the University of Alcala, Spain, in 2013. He is currently a principal researcher in data analytics and optimisation at TECNALIA (Spain), a visiting fellow at the Basque Center for Applied Mathematics (BCAM) and a part-time lecturer at the University of the Basque Country (UPV/EHU). His research interests gravitate on the use of descriptive, prescriptive and predictive algorithms for data mining and optimization in a diverse range of application fields such as Energy, Transport, Telecommunications, Health and Industry, among others. In these fields he has published more than 240 articles, co-supervised 8 Ph.D. theses, edited 6 books, coauthored 7 patents and participated/led more than 40 research projects. He is a Senior Member of the IEEE, and a recipient of the Biscay Talent prize for his academic career.",institutionString:"Tecnalia Research & Innovation",institution:null},{id:"278948",title:"Dr.",name:"Carlos Pedro",middleName:null,surname:"Gonçalves",slug:"carlos-pedro-goncalves",fullName:"Carlos Pedro Gonçalves",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRcmyQAC/Profile_Picture_1564224512145",biography:'Carlos Pedro Gonçalves (PhD) is an Associate Professor at Lusophone University of Humanities and Technologies and a researcher on Complexity Sciences, Quantum Technologies, Artificial Intelligence, Strategic Studies, Studies in Intelligence and Security, FinTech and Financial Risk Modeling. He is also a progammer with programming experience in:\n\nA) Quantum Computing using Qiskit Python module and IBM Quantum Experience Platform, with software developed on the simulation of Quantum Artificial Neural Networks and Quantum Cybersecurity;\n\nB) Artificial Intelligence and Machine learning programming in Python;\n\nC) Artificial Intelligence, Multiagent Systems Modeling and System Dynamics Modeling in Netlogo, with models developed in the areas of Chaos Theory, Econophysics, Artificial Intelligence, Classical and Quantum Complex Systems Science, with the Econophysics models having been cited worldwide and incorporated in PhD programs by different Universities.\n\nReceived an Arctic Code Vault Contributor status by GitHub, due to having developed open source software preserved in the \\"Arctic Code Vault\\" for future generations (https://archiveprogram.github.com/arctic-vault/), with the Strategy Analyzer A.I. module for decision making support (based on his PhD thesis, used in his Classes on Decision Making and in Strategic Intelligence Consulting Activities) and QNeural Python Quantum Neural Network simulator also preserved in the \\"Arctic Code Vault\\", for access to these software modules see: https://github.com/cpgoncalves. He is also a peer reviewer with outsanding review status from Elsevier journals, including Physica A, Neurocomputing and Engineering Applications of Artificial Intelligence. 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Currently working as an Assistant Professor in the Department of Mathematics, Institute of Applied Science, Mangalayatan University, Aligarh. She taught so many courses of Mathematics of UG and PG level. Her research Area of Expertise is Functional Analysis & Sequence Spaces. She has been working on Ideal Convergence of double sequence. She has published 17 research papers in National and International Journals including Cogent Mathematics, Filomat, Journal of Intelligent and Fuzzy Systems, Advances in Difference Equations, Journal of Mathematical Analysis, Journal of Mathematical & Computer Science etc. She has also reviewed few research papers for the and international journals. She is a member of Indian Mathematical Society.",institutionString:null,institution:null},{id:"414880",title:"Dr.",name:"Maryam",middleName:null,surname:"Vatankhah",slug:"maryam-vatankhah",fullName:"Maryam Vatankhah",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Borough of Manhattan Community College",country:{name:"United States of America"}}},{id:"414879",title:"Prof.",name:"Mohammad-Reza",middleName:null,surname:"Akbarzadeh-Totonchi",slug:"mohammad-reza-akbarzadeh-totonchi",fullName:"Mohammad-Reza Akbarzadeh-Totonchi",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Ferdowsi University of Mashhad",country:{name:"Iran"}}},{id:"414878",title:"Prof.",name:"Reza",middleName:null,surname:"Fazel-Rezai",slug:"reza-fazel-rezai",fullName:"Reza Fazel-Rezai",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"American Public University System",country:{name:"United States of America"}}},{id:"302698",title:"Dr.",name:"Yao",middleName:null,surname:"Shan",slug:"yao-shan",fullName:"Yao Shan",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Dalian University of Technology",country:{name:"China"}}},{id:"125911",title:"Prof.",name:"Jia-Ching",middleName:null,surname:"Wang",slug:"jia-ching-wang",fullName:"Jia-Ching Wang",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"National Central University",country:{name:"Taiwan"}}},{id:"357085",title:"Mr.",name:"P. 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Shukla",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Indian Institute of Technology Kanpur",country:{name:"India"}}},{id:"356823",title:"MSc.",name:"Seonghee",middleName:null,surname:"Min",slug:"seonghee-min",fullName:"Seonghee Min",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Daegu University",country:{name:"Korea, South"}}},{id:"353307",title:"Prof.",name:"Yoosoo",middleName:null,surname:"Oh",slug:"yoosoo-oh",fullName:"Yoosoo Oh",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:"Yoosoo Oh received his Bachelor's degree in the Department of Electronics and Engineering from Kyungpook National University in 2002. He obtained his Master’s degree in the Department of Information and Communications from Gwangju Institute of Science and Technology (GIST) in 2003. In 2010, he received his Ph.D. degree in the School of Information and Mechatronics from GIST. In the meantime, he was an executed team leader at Culture Technology Institute, GIST, 2010-2012. In 2011, he worked at Lancaster University, the UK as a visiting scholar. In September 2012, he joined Daegu University, where he is currently an associate professor in the School of ICT Conver, Daegu University. Also, he served as the Board of Directors of KSIIS since 2019, and HCI Korea since 2016. From 2017~2019, he worked as a center director of the Mixed Reality Convergence Research Center at Daegu University. From 2015-2017, He worked as a director in the Enterprise Supporting Office of LINC Project Group, Daegu University. His research interests include Activity Fusion & Reasoning, Machine Learning, Context-aware Middleware, Human-Computer Interaction, etc.",institutionString:null,institution:{name:"Daegu Gyeongbuk Institute of Science and Technology",country:{name:"Korea, South"}}},{id:"262719",title:"Dr.",name:"Esma",middleName:null,surname:"Ergüner Özkoç",slug:"esma-erguner-ozkoc",fullName:"Esma Ergüner Özkoç",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Başkent University",country:{name:"Turkey"}}},{id:"346530",title:"Dr.",name:"Ibrahim",middleName:null,surname:"Kaya",slug:"ibrahim-kaya",fullName:"Ibrahim Kaya",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Izmir Kâtip Çelebi University",country:{name:"Turkey"}}},{id:"419199",title:"Dr.",name:"Qun",middleName:null,surname:"Yang",slug:"qun-yang",fullName:"Qun Yang",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"University of Auckland",country:{name:"New Zealand"}}}]}},subseries:{item:{id:"7",type:"subseries",title:"Bioinformatics and Medical Informatics",keywords:"Biomedical Data, Drug Discovery, Clinical Diagnostics, Decoding Human Genome, AI in Personalized Medicine, Disease-prevention Strategies, Big Data Analysis in Medicine",scope:"Bioinformatics aims to help understand the functioning of the mechanisms of living organisms through the construction and use of quantitative tools. 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The considerable development of technology, including the computing power of computers, is also conducive to the development of bioinformatics, including personalized medicine. In an era of rapidly growing data volumes and ever lower costs of generating, storing and computing data, personalized medicine holds great promises. Modern computational methods used as bioinformatics tools can integrate multi-scale, multi-modal and longitudinal patient data to create even more effective and safer therapy and disease prevention methods. Main aspects of the topic are: Applying bioinformatics in drug discovery and development; Bioinformatics in clinical diagnostics (genetic variants that act as markers for a condition or a disease); Blockchain and Artificial Intelligence/Machine Learning in personalized medicine; Customize disease-prevention strategies in personalized medicine; Big data analysis in personalized medicine; Translating stratification algorithms into clinical practice of personalized medicine.",coverUrl:"https://cdn.intechopen.com/series_topics/covers/7.jpg",hasOnlineFirst:!0,hasPublishedBooks:!0,annualVolume:11403,editor:{id:"351533",title:"Dr.",name:"Slawomir",middleName:null,surname:"Wilczynski",slug:"slawomir-wilczynski",fullName:"Slawomir Wilczynski",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y000035U1loQAC/Profile_Picture_1630074514792",biography:"Professor Sławomir Wilczyński, Head of the Chair of Department of Basic Biomedical Sciences, Faculty of Pharmaceutical Sciences, Medical University of Silesia in Katowice, Poland. 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Editor-in-chief of the journal in the field of aesthetic medicine and dermatology - Aesthetica.",institutionString:null,institution:{name:"Medical University of Silesia",institutionURL:null,country:{name:"Poland"}}},editorTwo:null,editorThree:null,series:{id:"7",title:"Biomedical Engineering",doi:"10.5772/intechopen.71985",issn:"2631-5343"},editorialBoard:[{id:"5886",title:"Dr.",name:"Alexandros",middleName:"T.",surname:"Tzallas",slug:"alexandros-tzallas",fullName:"Alexandros Tzallas",profilePictureURL:"https://mts.intechopen.com/storage/users/5886/images/system/5886.png",institutionString:"University of Ioannina, Greece & Imperial College London",institution:{name:"University of Ioannina",institutionURL:null,country:{name:"Greece"}}},{id:"257388",title:"Distinguished Prof.",name:"Lulu",middleName:null,surname:"Wang",slug:"lulu-wang",fullName:"Lulu Wang",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRX6kQAG/Profile_Picture_1630329584194",institutionString:"Shenzhen Technology University",institution:{name:"Shenzhen Technology University",institutionURL:null,country:{name:"China"}}},{id:"225387",title:"Prof.",name:"Reda R.",middleName:"R.",surname:"Gharieb",slug:"reda-r.-gharieb",fullName:"Reda R. 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